As the silicon wafers used by semiconductor manufacturers increase in size, more accurate control of gas flow through the manufacturing equipment has become more critical for the precise fabrication of circuits on the wafers. Mass flow controllers and mass flow meters are typically used to control and monitor gas flow through a semiconductor manufacturing tool. In the manufacturing process, if the controller or monitor detects a fluctuation in gas pressure that is outside predefined operating parameters for a particular manufacturing tool, an alarm is usually triggered to shut down the tool. This is often costly to manufacturers as it reduces throughput and because the wafer batch being processed when the tool is shut down is usually ruined. Even if the batch is not ruined or damaged, however, additional wafer batches cannot be processed until the tool is brought back online. This is even more annoying and inefficient when the shutdown of the manufacturing tool is due to a false alarm (i.e. a non-critical event).
In this regard, conventional mass flow controllers and mass flow meters are deficient because they often produce false alarms due to noise or transient pressure spikes that do impact the manufacturing process by exceeding the manufacturing tolerances. In a mass flow controller, for example, a thermal flow sensor reads the flow of gas to a manufacturing tool. The conventional thermal flow sensor, however, has several limitations, one of these being that the time constant used by the thermal flow sensor to read gas flow through the system is much longer than the desired time necessary to control the flow. In other words, by the time the thermal flow sensor reads an event it is too late to react to the event by controlling the gas flow or shutting down the system. One method of accelerating or predicting gas flow faster than provided by the conventional thermal flow sensor is to derive a weighted first derivative of the signal generated by the thermal flow sensor and add the weighted derivative to the signal, producing an indicated flow. The indicated flow is then compared to a set point for the manufacturing tool. If there is an error (e.g., if the indicated flow does not match the set point) a gas flow valve will typically be throttled so that the indicated flow matches the set point, yielding zero error, or if the error is greater than a predetermined level, the tool is shut down.
FIG. 1 is a diagrammatic representation illustrating a prior art system 100 for regulating gas flow. In system 100, the actual gas flow 105 is monitored by thermal flow sensor 110. Thermal flow sensor 110 samples the actual flow 105 and outputs signal 115 representing the actual gas flow 105. To accelerate or predict the flow faster, a derivative controller 120 (“D-controller” 120), derives a weighted first derivative of signal 117 by multiplying a predetermined gain times the first derivative of signal 115 to produce derivative signal 117. Derivative signal 117 is then added to signal 115 via summer 119 to produce indicated flow 125.
Indicated flow 125 can be compared to a set point for the manufacturing tool involved (e.g., at a comparator 127). If the indicated flow does not match the set point (e.g., if an error is detected) a gas flow valve current or actuator current (signal 137) is generated to throttle valve 130, thereby regulating the actual flow 105 and yielding a zero error. The throttling of valve 130 is achieved via a proportional and integral controller 135 (“P&I controller” 135). Thus, while thermal flow sensor 110 operates on a time scale longer than desired for the control of actual flow 105, the output of thermal flow sensor 115 is manipulated to achieve faster response, thus providing a means to control the gas flow, if a critical event is encountered, in a timely manner avoiding damage to the current work product.
While an improvement in monitoring and controlling of gas flows, these prior art systems still have several shortcomings. Thermal flow sensor 110 is located at only one location in actual flow path 105 and can only detect local flow. Thus, thermal flow sensor 110 may detect local instabilities in actual flow 105 that are not representative of the flow as a whole. For example, if thermal flow sensor 110 is located at an area of local turbulence (e.g., near a bend in the gas flow path), it may pick up local instabilities that are not representative of what is actually occurring downstream at the fabrication chamber. Furthermore, the sensor may itself cause eddy currents at its mouth, thereby causing thermal flow sensor 110 to produce an inaccurate or noisy signal 115. The prior art system is further deficient in that derivative signal 117 typically enhances any noise present in signal 115. Thus, indicated flow 125 includes enhanced noise that is often not representative of actual flow 105.
When indicated flow 125 is compared to the set point for a tool (typically after indicated flow 125 reaches steady state), if the mismatch between indicated flow 125 and the set point is greater than the threshold valve, an alarm is generated triggering a shut down of the tool, typically ruining the batch of wafers upon which work is currently being performed. This mismatch often is not due to levels of noise in actual flow 105, but may be caused by the enhanced noise present in derivative signal 117. To compensate for noise in indicated flow 125, P&I controller 135 includes filtering capabilities. However, the filtering capabilities of conventional mass flow meters are limited because they can not adequately filter noise that spans over a broad frequency range.
Furthermore, these prior art systems are limited because they generally do not handle pressure spikes well. If there is a brief pressure spike in actual flow 105, the spike, which would be represented in indicated flow 125, can cause an alarm condition (e.g., can cause the tool to shut down) even if the spike would not affect the production process by exceeding its tolerances. Thus, the prior art systems cause unnecessary downtime due to noise or transient pressure differences.
Therefore, a need exists for a filter that is independent of the noise frequency, is less affected by transient spikes and does not compromise the response time of the tool to which it is being applied.